Railway AI — Intelligent Planning Engine

Anyone who has traveled on Indian Railways knows that punctuality and safety are a delicate balancing act. Every day, thousands of kilometers of track, overhead electric traction wires (OHE), and signal systems take a heavy pounding from passenger expresses and heavy freight trains.

To keep everything running safely, railway maintenance crews need time on the tracks. In railway terms, this is called a "maintenance block" — a window of time (usually 2 to 6 hours) where a track section is closed to train traffic so engineers can replace rails, inspect signals, or adjust high-voltage wires.

What Does the Engine Actually Do?

Corridor Digital Twin & Real Evidence

Focused on the New Delhi to Mumbai Central Golden Quadrilateral corridor (1,384 km). The system automatically resolves historical and modern Indian Railways station aliases:

The platform incorporates 11 serialized model artifacts tuned for precision-recall area under curve (PR-AUC) with isotonic probability calibration:

The block optimizer models track time as discrete 30-minute intervals across the planning horizon, formulating maintenance scheduling as an exact Constraint Satisfaction Problem (CSP):

When track engineers shut down a section between Mathura and Agra for rail renewal, the engine scans for pending signal checkups (S&T) and overhead traction inspections (TRD) in that exact section. It bundles them into the same block window:

Multi-Horizon Planning: Daily to Monthly

The engine operates across four distinct planning horizons to support tactical repairs and strategic track maintenance:

Transparent & Human-in-the-Loop Explainability

No black-box decisions. Every block recommendation is accompanied by human-readable reason tags:

The engine exposes a unified interface in src/services/ml_engine.py:

import pandas as pd
from src.services.ml_engine import RailwayMLEngine

# 1. Initialize engine (loads all 6 models into memory)
engine = RailwayMLEngine()

# 2. Check engine health
health = engine.health()
print(f"Engine status: {health['status']} (Models loaded: {health['models_loaded']})")

# 3. Create worklist of pending maintenance tasks
tasks = pd.DataFrame([
    {
        "task_id": "TASK-001",
        "section_id": "NDL-MTJ-01",
        "department": "ENGINEERING",
        "condition_score": 38.0,
        "criticality": 4,
        "urgency": 5,
        "days_overdue": 14,
        "estimated_duration_hours": 3.0,
        "required_manpower": 12
    }
])

# 4. Score tasks with calibrated ML failure risk
scored = engine.predict(tasks)
print(scored[["task_id", "maintenance_decision_score", "decision_reasons"]])

# 5. Generate conflict-free weekly block schedule
plan = engine.generate_block_plan(tasks, horizon_type="weekly")
print(f"Scheduled {len(plan['scheduled_tasks'])} tasks across available windows!")

The benchmark validator (BenchmarkValidator) executes automated comparisons between the Railway AI optimizer and the industry-standard FIFO baseline: